The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against LLM Jailbreaks and Prompt Injections

Safety in Embodied AI: Risks, Attacks, and Defenses

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses | 500+ Papers | Perception, Cognition, Planning, Interaction, Agentic System

Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

This paper explores the concept of Indirect Prompt Injection attacks on Large Language Models (LLMs) through their integration with various applications. It identifies significant security risks, including remote data theft and ecosystem contamination, present in both real-world and synthetic applications

Attention Tracker: Detecting Prompt Injection Attacks in LLMs

NAACL 2025 Findings paper detecting prompt injection by tracking attention distribution shifts — no modification to the underlying model required, making it deployable as a wrapper on any LLM

The Landscape of Prompt Injection Threats in LLM Agents (SoK)

Feb 2026 systematization-of-knowledge paper with a unified taxonomy covering attack payload strategies (heuristic vs. optimisation-based) and defense intervention stages (text, model, execution). Introduces the AgentPI benchmark for context-dependent agent tasks that all prior benchmarks ignored